Spinal cord data
Contents
Spinal cord data¶
Spinal cord CSA¶
# Python imports
from pathlib import Path
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Get data from github repo
dir = Path('spinalcord_results')
if dir.exists() is False:
# Get data from github repo
# Get data from github repo
!wget -O spinalcord_results.zip https://github.com/courtois-neuromod/anat-processing/releases/download/r20210610/spinalcord_results.zip
!unzip -j spinalcord_results.zip -d ./spinalcord_results/
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Load the data
df_t1 = pd.read_csv("./spinalcord_results/csa-SC_T1w.csv", converters={'project_id': lambda x: str(x)})
df_t2 = pd.read_csv("./spinalcord_results/csa-SC_T2w.csv", converters={'project_id': lambda x: str(x)})
# Insert new columns for Subject and Session and start inserting values
df_t1.insert(0, "Subject", "Any")
df_t1.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_t1.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_t1.at[index, 'Subject'] = subject
df_t1.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_2 = df_t1.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_1 = []
mean_ap_matrix_1 = []
mean_rl_matrix_1 = []
mean_angle_matrix_1 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_2.loc[df_2['Subject'] == i+1]
mean_area_ses = []
mean_ap_ses = []
mean_rl_ses = []
mean_angle_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
mean_ap = -100
mean_rl = -100
mean_angle = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
MEAN_diameter_AP = row['MEAN(diameter_AP)']
MEAN_diameter_RL = row['MEAN(diameter_RL)']
MEAN_angle_AP = row['MEAN(angle_AP)']
# Store values
mean_area = MEAN_area
mean_ap = MEAN_diameter_AP
mean_rl = MEAN_diameter_RL
mean_angle = MEAN_angle_AP
# Append values to lists for sessions
mean_area_ses.append(mean_area)
mean_ap_ses.append(mean_ap)
mean_rl_ses.append(mean_rl)
mean_angle_ses.append(mean_angle)
# Append session lists to main matrices for each metric
mean_area_matrix_1.append(mean_area_ses)
mean_ap_matrix_1.append(mean_ap_ses)
mean_rl_matrix_1.append(mean_rl_ses)
mean_angle_matrix_1.append(mean_angle_ses)
# Insert new columns for Subject and Session and start inserting values
df_t2.insert(0, "Subject", "Any")
df_t2.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_t2.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_t2.at[index, 'Subject'] = subject
df_t2.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_3 = df_t2.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_2 = []
mean_ap_matrix_2 = []
mean_rl_matrix_2 = []
mean_angle_matrix_2 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_3.loc[df_3['Subject'] == i+1]
mean_area_ses = []
mean_ap_ses = []
mean_rl_ses = []
mean_angle_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
mean_ap = -100
mean_rl = -100
mean_angle = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
MEAN_diameter_AP = row['MEAN(diameter_AP)']
MEAN_diameter_RL = row['MEAN(diameter_RL)']
MEAN_angle_AP = row['MEAN(angle_AP)']
# Store values
mean_area = MEAN_area
mean_ap = MEAN_diameter_AP
mean_rl = MEAN_diameter_RL
mean_angle = MEAN_angle_AP
# Append values to lists for sessions
mean_area_ses.append(mean_area)
mean_ap_ses.append(mean_ap)
mean_rl_ses.append(mean_rl)
mean_angle_ses.append(mean_angle)
# Append session lists to main matrices for each metric
mean_area_matrix_2.append(mean_area_ses)
mean_ap_matrix_2.append(mean_ap_ses)
mean_rl_matrix_2.append(mean_rl_ses)
mean_angle_matrix_2.append(mean_angle_ses)
# Functions for mean and std
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.7f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.7f}'.format(np.std(mean_list)))
return std
# Functions for getting y label limits
def get_limit_max(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.max(mean_list) + (np.max(mean_list)-np.min(mean_list))/4
def get_limit_min(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.min(mean_list) - (np.max(mean_list)-np.min(mean_list))/4
# Function for drawing lines
def draw_line(mean_matrix, fig, metric, statistic, visible=False):
# Define x values
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1] # reversed list
x_region = len(x)
if metric == 'T1w' or metric == 'T2w':
if statistic == 'mean':
# Define name and color for mean line
name = 'T<sub>1</sub>w' if metric == 'T1w' else 'T<sub>2</sub>w'
color = "rgb(31, 119, 180)" if metric == 'T1w' else "rgb(255, 187, 120)"
# Calculate the mean value
line = get_mean(mean_matrix)
# Add the mean for given matrix
fig.add_trace(go.Scatter(x=x,
y=[line]*x_region,
mode='lines',
visible = visible,
name=name,
opacity=0.5,
line=dict(color=color,
width=2,
dash='dot')))
elif statistic == 'std':
# Define name and color for STD region
name = 'T<sub>1</sub>w STD' if metric == 'T1w' else 'T<sub>2</sub>w STD'
fillcolor = 'rgba(31, 119, 180,0.15)' if metric == 'T1w' else 'rgba(255, 187, 120,0.15)'
# Calculate the mean, std value
line = get_mean(mean_matrix)
std = get_std(mean_matrix)
# Add STD for given matrix
fig.add_trace(go.Scatter(
x=x+x_rev,
y=[line+std]*x_region+[line-std]*x_region,
fill='toself',
visible = visible,
fillcolor=fillcolor,
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name=name,
))
else:
if statistic == 'mean':
# Define name and color for mean line
name = metric
color = "rgb(31, 119, 180)"
# Calculate the mean value
line = get_mean(mean_matrix)
# Add the mean for given matrix
fig.add_trace(go.Scatter(x=x,
y=[line]*x_region,
mode='lines',
visible = visible,
name=name,
opacity=0.5,
line=dict(color=color,
width=2,
dash='dot')))
elif statistic == 'std':
# Define name and color for STD region
name = metric + " STD"
fillcolor = 'rgba(31, 119, 180,0.15)'
# Calculate the mean, std value
line = get_mean(mean_matrix)
std = get_std(mean_matrix)
# Add STD for given matrix
fig.add_trace(go.Scatter(
x=x+x_rev,
y=[line+std]*x_region+[line-std]*x_region,
fill='toself',
visible = visible,
fillcolor=fillcolor,
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name=name,
))
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define lists needed for plotting
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for fonts
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN [area] --- > T1w
for trace in range(0, len(mean_area_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_1[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [area] --- > T2w
for trace in range(0, len(mean_area_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_2[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [AP] --- > T1w
for trace in range(0, len(mean_ap_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace == 0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_ap_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean [AP]: <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [AP] --- > T2w
for trace in range(0, len(mean_ap_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_ap_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean [AP]: <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
marker_symbol=symbols[5],
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [RL] --- > T1w
for trace in range(0, len(mean_rl_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_rl_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean (RL): <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [RL] --- > T2w
for trace in range(0, len(mean_rl_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_rl_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean (RL): <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [angle_AP] --- > T1w
for trace in range(0, len(mean_angle_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_angle_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean (angle_AP): <i> %{y: .2f} </i>°" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [angle_AP] --- > T2w
for trace in range(0, len(mean_angle_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_angle_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean (angle_AP): <i> %{y: .2f} </i>°" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Calculate means
draw_line(mean_area_matrix_1, figb, 'T1w', 'mean', visible=True) # T1w mean area --- mean
draw_line(mean_area_matrix_2, figb, 'T2w', 'mean', visible=True) # T2w mean area --- mean
draw_line(mean_ap_matrix_1, figb, 'T1w', 'mean') # T1w mean AP --- mean
draw_line(mean_ap_matrix_2, figb, 'T2w', 'mean') # T2w mean AP --- mean
draw_line(mean_rl_matrix_1, figb, 'T1w', 'mean') # T1w mean RL --- mean
draw_line(mean_rl_matrix_2, figb, 'T2w', 'mean') # T2w mean RL --- mean
draw_line(mean_angle_matrix_1, figb, 'T1w', 'mean') # T1w angle --- mean
draw_line(mean_angle_matrix_2, figb, 'T2w', 'mean') # T2w angle --- mean
draw_line(mean_area_matrix_1, figb, 'T1w', 'std', visible=True) # T1w mean area --- std
draw_line(mean_area_matrix_2, figb, 'T2w', 'std', visible=True) # T2w mean area --- std
draw_line(mean_ap_matrix_1, figb, 'T1w', 'std') # T1w mean AP --- std
draw_line(mean_ap_matrix_2, figb, 'T2w', 'std') # T2w mean AP --- std
draw_line(mean_rl_matrix_1, figb, 'T1w', 'std') # T1w mean RL --- std
draw_line(mean_rl_matrix_2, figb, 'T2w', 'std') # T2w mean RL --- std
draw_line(mean_angle_matrix_1, figb, 'T1w', 'std') # T1w angle --- std
draw_line(mean_angle_matrix_2, figb, 'T2w', 'std') # T2w angle --- std
figb.update_layout(title = ' Spinal cord CSA [T<sub>1</sub>w/T<sub>2</sub>w]',
updatemenus=[
dict(
active = 0,
x=1.27,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="Mean (area)",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*36 + [True]*2 + [False]*6 + [True]*2 + [False]*6},
{"yaxis": dict(range=[get_limit_min(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0)), get_limit_max(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0))],
title='Mean (area) [mm<sup>2</sup>]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (AP)",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*12 + [False]*24 + [False]*2 + [True]*2 +[False]*4 + [False]*2 + [True]*2 +[False]*4},
{"yaxis": dict(range=[get_limit_min(np.append(mean_ap_matrix_1, mean_ap_matrix_2, axis=0)), get_limit_max(np.append(mean_ap_matrix_1, mean_ap_matrix_2, axis=0))],
title='Mean (AP) [mm]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (RL)",
method="update",
args=[{"visible": [True] + [False]*24 + [True]*12 + [False]*12 + [False]*4 + [True]*2 +[False]*2 + [False]*4 + [True]*2 +[False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_rl_matrix_1, mean_rl_matrix_2, axis=0)), get_limit_max(np.append(mean_rl_matrix_1, mean_rl_matrix_2, axis=0))],
title='Mean (RL) [mm]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (angle)",
method="update",
args=[{"visible": [True] + [False]*36 + [True]*12 + [False]*6 + [True]*2 + [False]*6 + [True]*2 },
{"yaxis": dict(range=[get_limit_min(np.append(mean_angle_matrix_1, mean_angle_matrix_2, axis=0)), get_limit_max(np.append(mean_angle_matrix_1, mean_angle_matrix_2, axis=0))],
title='Mean (angle) [°]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='Mean (area) [mm<sup>2</sup>]',
yaxis=dict(range=[get_limit_min(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0)), get_limit_max(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig.html', config = config)
display(HTML('new-fig.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1950/136316473.py:7: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
--2022-05-25 18:12:39-- https://github.com/courtois-neuromod/anat-processing/releases/download/r20210610/spinalcord_results.zip
Resolving github.com (github.com)... 140.82.114.4
Connecting to github.com (github.com)|140.82.114.4|:443... connected.
HTTP request sent, awaiting response...
302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/9e444c00-c9e2-11eb-880c-f9aa2a43f100?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220525%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220525T181240Z&X-Amz-Expires=300&X-Amz-Signature=c37d6a5d8d2655fa46630ed314322fd8d236ef31099f523b43caf7cc79ae206c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dspinalcord_results.zip&response-content-type=application%2Foctet-stream [following]
--2022-05-25 18:12:40-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/9e444c00-c9e2-11eb-880c-f9aa2a43f100?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220525%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220525T181240Z&X-Amz-Expires=300&X-Amz-Signature=c37d6a5d8d2655fa46630ed314322fd8d236ef31099f523b43caf7cc79ae206c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dspinalcord_results.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response...
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Archive: spinalcord_results.zip
inflating: ./spinalcord_results/DWI_RD.csv
inflating: ./spinalcord_results/DWI_FA.csv
inflating: ./spinalcord_results/csa-SC_T2w.csv
inflating: ./spinalcord_results/DWI_MD.csv
inflating: ./spinalcord_results/MTR.csv
inflating: ./spinalcord_results/MTsat.csv
inflating: ./spinalcord_results/csa-GM_T2s.csv
inflating: ./spinalcord_results/csa-SC_T1w.csv
inflating: ./spinalcord_results/._csa-SC_T1w.csv
inflating: ./spinalcord_results/T1.csv
Gray matter CSA¶
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Load the data
df_3 = pd.read_csv("./spinalcord_results/csa-GM_T2s.csv", converters={'project_id': lambda x: str(x)})
# Insert new columns for Subject and Session and start inserting values
df_3.insert(0, "Subject", "Any")
df_3.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_3.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_3.at[index, 'Subject'] = subject
df_3.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_3 = df_3.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_1 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_3.loc[df_3['Subject'] == i+1]
mean_area_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
# Store values
mean_area = MEAN_area
# Append values to lists for sessions
mean_area_ses.append(mean_area)
# Append session lists to main matrices for each metric
mean_area_matrix_1.append(mean_area_ses)
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN [area] --- > T1w
for trace in range(0, len(mean_area_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_1[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>2</sub>s',
marker_color="rgb"+str(tableau20[0])))
# Calculate mean and std
draw_line(mean_area_matrix_1, figb, 'T<sub>2</sub>s', 'mean', visible=True) # T2s mean area --- mean
draw_line(mean_area_matrix_1, figb, 'T<sub>2</sub>s', 'std', visible=True) # T2s mean area --- std
figb.update_layout(title = 'Spinal cord gray matter CSA',
title_x = 0.5,
showlegend=False,
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='Mean (area) [mm<sup>2</sup>]',
yaxis=dict(range=[get_limit_min(mean_area_matrix_1), get_limit_max(mean_area_matrix_1)],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 640,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-2.html', config = config)
display(HTML('new-fig-2.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1950/582358489.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
White matter qMRI¶
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Read data for (DWI_FA, DWI_MD, DWI_RD, MTR, MTsat, T1)
df_dwi_fa = pd.read_csv("./spinalcord_results/DWI_FA.csv", converters={'project_id': lambda x: str(x)})
df_dwi_md = pd.read_csv("./spinalcord_results/DWI_MD.csv", converters={'project_id': lambda x: str(x)})
df_dwi_rd = pd.read_csv("./spinalcord_results/DWI_RD.csv", converters={'project_id': lambda x: str(x)})
df_mtr = pd.read_csv("./spinalcord_results/MTR.csv", converters={'project_id': lambda x: str(x)})
df_mtsat = pd.read_csv("./spinalcord_results/MTsat.csv", converters={'project_id': lambda x: str(x)})
df_t1_2 = pd.read_csv("./spinalcord_results/T1.csv", converters={'project_id': lambda x: str(x)})
# Function for reading session values
def get_sessions_values(df, metric):
'''
:param df: pandas dataframe with results
:param metric: column in dataframe
:return: session values as matrix
'''
df.insert(0, "Subject", "Any")
df.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df.at[index, 'Subject'] = subject
df.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_2 = df.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
matrix_1 = []
# Get the values for all desired column (metric)
for i in range(0, 6, 1):
sub_values = df_2.loc[df_2['Subject'] == i + 1]
ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j + 1]
temp = -100
for index, row in ses_values.iterrows():
# Read values
my_value = row[metric]
# Store values
temp = my_value
# Append values to lists for sessions
ses.append(temp)
# Append session lists to main matrices for each metric
matrix_1.append(ses)
return matrix_1
# Get values for sessions, store in lists
matrix_dwi_fa = get_sessions_values(df=df_dwi_fa,metric="WA()")
matrix_dwi_md = get_sessions_values(df=df_dwi_md, metric="WA()")
matrix_dwi_rd = get_sessions_values(df=df_dwi_rd, metric="WA()")
matrix_mtr = get_sessions_values(df=df_mtr, metric="WA()")
matrix_mtsat = get_sessions_values(df=df_mtsat, metric="WA()")
matrix_t1_2 = get_sessions_values(df=df_t1_2, metric="WA()")
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define lists needed for plotting
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add DWI_FA
for trace in range(0, len(matrix_dwi_fa)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_fa[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_FA',
marker_color="rgb"+str(tableau20[0])))
# Add DWI_MD
for trace in range(0, len(matrix_dwi_md)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_md[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .6f} </i> mm<sup>2</sup>/s" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_MD',
marker_color="rgb"+str(tableau20[0])))
# Add DWI_RD
for trace in range(0, len(matrix_dwi_rd)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_rd[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .6f} </i> mm<sup>2</sup>/s" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_RD',
marker_color="rgb"+str(tableau20[0])))
# Add MTR
for trace in range(0, len(matrix_mtr)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_mtr[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MTsat
for trace in range(0, len(matrix_mtsat)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_mtsat[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'MTsat',
marker_color="rgb"+str(tableau20[0])))
# Add T1
for trace in range(0, len(matrix_t1_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_t1_2[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .7f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub>',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
draw_line(matrix_dwi_fa, figb, 'DWI_FA', 'mean', visible=True) # DWI_FA --- mean
draw_line(matrix_dwi_md, figb, 'DWI_MD', 'mean') # DWI_MD --- mean
draw_line(matrix_dwi_rd, figb, 'DWI_RD', 'mean') # DWI_RD --- mean
draw_line(matrix_mtr, figb, 'MTR', 'mean') # MTR --- mean
draw_line(matrix_mtsat, figb, 'MTsat', 'mean') # MTsat --- mean
draw_line(matrix_t1_2, figb, 'T<sub>1</sub>', 'mean') # T1 --- mean
# Calculate stds
draw_line(matrix_dwi_fa, figb, 'DWI_FA', 'std', visible=True) # DWI_FA --- mean
draw_line(matrix_dwi_md, figb, 'DWI_MD', 'std') # DWI_MD --- mean
draw_line(matrix_dwi_rd, figb, 'DWI_RD', 'std') # DWI_RD --- mean
draw_line(matrix_mtr, figb, 'MTR', 'std') # MTR --- mean
draw_line(matrix_mtsat, figb, 'MTsat', 'std') # MTsat --- mean
draw_line(matrix_t1_2, figb, 'T<sub>1</sub>', 'std') # T1 --- mean
figb.update_layout(title = 'Spinal cord qMRI microstructure ',
updatemenus=[
dict(
active = 0,
x=1.24,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="DWI_FA",
method="update",
args=[{"visible": [True] + [True]*6 + [False]*30 + [True]*1 + [False]*5 + [True]*1 + [False]*5},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_fa), get_limit_max(matrix_dwi_fa)],
title='DWI_FA [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="DWI_MD",
method="update",
args=[{"visible": [True] + [False]*6 + [True]*6 + [False]*24 + [False]*1 + [True]*1 +[False]*4 + [False]*1 + [True]*1 +[False]*4},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_md), get_limit_max(matrix_dwi_md)],
title='DWI_MD [mm<sup>2</sup>/s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="DWI_RD",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*18 + [False]*2 + [True]*1 +[False]*3 + [False]*2 + [True]*1 +[False]*3},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_rd), get_limit_max(matrix_dwi_rd)],
title=' DWI_RD [mm<sup>2</sup>/s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*12 + [False]*3 + [True]*1 +[False]*2 + [False]*3 + [True]*1 +[False]*2},
{"yaxis": dict(range=[get_limit_min(matrix_mtr), get_limit_max(matrix_mtr)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*24 + [True]*6 + [False]*6 + [False]*4 + [True]*1 +[False]*1 + [False]*4 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(matrix_mtsat), get_limit_max(matrix_mtsat)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [False]*30 + [True]*6 + [False]*5 + [True]*1 + [False]*5 + [True]*1 },
{"yaxis": dict(range=[get_limit_min(matrix_t1_2), get_limit_max(matrix_t1_2)],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='DWI_FA [a.u.]',
yaxis=dict(range=[get_limit_min(matrix_dwi_fa), get_limit_max(matrix_dwi_fa)],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-3.html', config = config)
display(HTML('new-fig-3.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1950/3287582139.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display